README.md

---
license: bigscience-openrail-m
size_categories:
- 1K<n<10K
tags:
- synthetic
---

# rerandomization-benchmarks

Replication dataset for the benchmark and diagnostic analyses in  
**Goldstein, Jerzak, Kamat & Zhu (2025), _“fastrerandomize: Fast Rerandomization Using Accelerated Computing”_.**

---

## Project & Paper Links

- **Paper (preprint):** <https://arxiv.org/abs/2501.07642>  
- **Software repository:** <https://github.com/cjerzak/fastrerandomize-software>  
- **Package name:** `fastrerandomize` (R)

---

##  What’s in this dataset?

The dataset contains **simulation-based benchmark results** used to compare:

- Different **hardware backends**  
  - `M4-CPU` (Apple M4 CPU, via JAX/XLA)  
  - `M4-GPU` (Apple M4 GPU / METAL)  
  - `RTX4090` (NVIDIA CUDA GPU)  
  - `BaseR` (non-accelerated R baseline)  
  - `jumble` (the `jumble` package as an alternative rerandomization implementation)

- Different **problem scales**  
  - Sample sizes: `n_units ∈ {10, 100, 1000}`  
  - Covariate dimensions: `k_covars ∈ {10, 100, 1000}`  
  - Monte Carlo draw budgets: `maxDraws ∈ {1e5, 2e5}`  
  - Exact vs approximate linear algebra: `approximate_inv ∈ {TRUE, FALSE}`  

- Different **rerandomization specifications**  
  - Acceptance probability targets (via `randomization_accept_prob`)  
  - Use or non-use of fiducial intervals (`findFI`)  

Each row corresponds to a particular Monte Carlo configuration and summarizes:

1. **Design & simulation settings** (e.g., `n_units`, `k_covars`, `maxDraws`, `treatment_effect`)  
2. **Performance metrics** (e.g., runtime for randomization generation and testing)  
3. **Statistical diagnostics** (e.g., p-value behavior, coverage, FI width)  
4. **Hardware & system metadata** (CPU model, number of cores, OS, etc.)

These data were used to:

- Produce the **runtime benchmark figures** (CPU vs GPU vs baseline R / `jumble`)  
- Compute **speedup factors** and **time-reduction summaries**  
- Feed into macros such as `\FRRMaxSpeedupGPUvsBaselineOverall`, `\FRRGPUVsCPUTimeReductionDthousandPct`, etc., which are then read from `./Figures/bench_macros.tex` in the paper.

---

##  Files & Structure

*(Adjust this section to match exactly what you upload to Hugging Face; here is a suggested structure.)*

- `VaryNAndD_main.csv`  
  Aggregated benchmark/simulation results across all configurations used in the paper.

- `VaryNAndD_main.parquet` (optional)  
  Parquet version of the same table (faster to load in many environments).

---

## Main Columns (schema overview)

Below is an overview of the most important columns you will encounter in `VaryNAndD_main.*`.  

### Core design variables

- `treatment_effect` – Constant treatment effect used in the simulation (e.g., `0.1`).  
- `SD_inherent` – Baseline SD of potential outcomes (`SD_inherent` in `GenerateCausalData`).  
- `n_units` – Total number of experimental units.  
- `k_covars` – Number of covariates.
- `prob_accept` – Target acceptance probability (`randomization_accept_prob`).  
- `maxDraws` – Maximum number of candidate randomizations drawn (e.g., `1e5`, `2e5`).  
- `findFI` – Logical (`TRUE`/`FALSE`): whether fiducial intervals were computed.  
- `approximate_inv` – Logical (`TRUE`/`FALSE`): whether approximate inverse / stabilized linear algebra was used.  
- `Hardware` – Hardware / implementation label, recoded in `FastRR_PlotFigs.R` to:  
  - `"M4-CPU"`  (was `"CPU"`)  
  - `"M4-GPU"`  (was `"METAL"`)  
  - `"RTX4090"` (was `"NVIDIA"`)  
  - `"jumble"`  (was `"AltPackage"`)  
  - `"BaseR"`   (pure R baseline)  
- `monte_i` – Monte Carlo replication index.

### Timing and hardware metadata

Timing quantities are used to produce the benchmark plots in the paper:

- `t_GenerateRandomizations` – Time (seconds) spent generating randomization pools.  
- `t_RandomizationTest` – Time (seconds) spent on randomization-based inference.  
- `randtest_time` – Duplicated / convenience version of `t_RandomizationTest` in some contexts.  
- `sysname`, `machine`, `hardware_version` – OS and machine-level metadata (`Sys.info()`).  
- `nCores` – Number of CPU cores from `benchmarkme::get_cpu()`.  
- `cpuModel` – CPU model name from `benchmarkme::get_cpu()`.

---

## How to use the dataset

### In Python (via `datasets`)

```python
from datasets import load_dataset

ds = load_dataset("YOUR_USERNAME/rerandomization-benchmarks", split="train")
print(ds)
print(ds.column_names)
````

Or directly with `pandas`:

```python
import pandas as pd

df = pd.read_csv("VaryNAndD_main.csv")
df.head()
```

### In R

```r
library(data.table)

bench <- fread("VaryNAndD_main.csv")
str(bench)

# Example: reproduce summaries by hardware and problem size
bench[, .(
  mean_t_generate = mean(t_GenerateRandomizations, na.rm = TRUE),
  mean_t_test     = mean(t_RandomizationTest, na.rm = TRUE)
), by = .(Hardware, n_units, k_covars, maxDraws, approximate_inv)]
```

You can then:

* Recreate runtime comparisons across hardware platforms.
* Explore how acceptance probability, dimension, and sample size interact.
* Use the timing information as inputs for your own design/planning calculations.

---

## Citation

If you use this dataset, please cite the paper:

```bibtex
@misc{goldstein2025fastrerandomizefastrerandomizationusing,
      title        = {fastrerandomize: Fast Rerandomization Using Accelerated Computing},
      author       = {Rebecca Goldstein and Connor T. Jerzak and Aniket Kamat and Fucheng Warren Zhu},
      year         = {2025},
      eprint       = {2501.07642},
      archivePrefix= {arXiv},
      primaryClass = {stat.CO},
      url          = {https://arxiv.org/abs/2501.07642}
}
```

---

## Contact

For questions about the paper, software, or dataset:

* Corresponding author: **Connor T. Jerzak** – [connor.jerzak@austin.utexas.edu](mailto:connor.jerzak@austin.utexas.edu)
* Issues & contributions: please use the GitHub repository issues page for `fastrerandomize`.

---